code: def __init__(self):
      super(Generator, self).__init__()
  
      def convlayer(n_input, n_output, k_size=4, stride=2, padding=0):
          block = [
              nn.ConvTranspose2d(
                  n_input,
                  n_output,
                  kernel_size=k_size,
                  stride=stride,
                  padding=padding,
                  bias=False,
              ),
              nn.BatchNorm2d(n_output),
              nn.ReLU(inplace=True),
          ]
          return block
  
      self.model = nn.Sequential(
          *convlayer(
              opt.latent_dim, 1024, 4, 1, 0
          ),  # Fully connected layer via convolution.
          *convlayer(1024, 512, 4, 2, 1),
          *convlayer(512, 256, 4, 2, 1),
          *convlayer(256, 128, 4, 2, 1),
          *convlayer(128, 64, 4, 2, 1),
          *convlayer(64, 32, 4, 2, 1),
          nn.ConvTranspose2d(32, opt.channels, 4, 2, 1),
          nn.Tanh(),
      )
      """
          There is a slight error in v2 of the relativistic gan paper, where
          the architecture goes from 128>64>32 but then 64>3.
          """
  
file_path: gans-with-pytorch/ralsgan/ralsgan.py
imports: import argparse
  import numpy
  import torch
  from torch import nn, optim
  from torch.autograd.variable import Variable
  from torchvision import transforms, datasets
  import os
label_desc: Use image node_id 1 to create a new Generator object from inherited base classes: nn with example: obj = Generator()